Iris Flower Classification

This repository contains the implementation for Task 1 of the CodeAlpha Data Science Internship.

Project Overview

The goal is to build a machine learning model to classify iris flowers into three distinct species (Setosa, Versicolor, Virginica) using their sepal and petal measurements.

Workflow

1. Loaded the standard Iris dataset via Scikit-learn.
2. Split features and target labels into training (80%) and testing (20%) sets.
3. Trained a K-Nearest Neighbors (KNN) classifier.
4. Evaluated model accuracy and generated a confusion matrix.

Results

• Algorithm: K-Nearest Neighbors (k=5)
• Accuracy: ~96.67% to 100%

How to Run

1. Install dependencies: pip install -r requirements.txt
2. Run the script: python app.py